Grasping an object by a robot manipulator generally requires that the 3-D pose of the object be known. In many applications in robotics, the geometry of the task environment is uncertain so that the position and orientation of a target object may be known only approximately. In order to grasp an object successfully and safely, the actual pose of the object must be determined relative to the robot hand or gripper using some form of vision sensing. Many solutions to this problem work only under certain conditions. For example, a system that is capable of recognizing an object having a simple polygonal shape may not be capable of recognizing an object whose shape is made up of complex curved surfaces. As well, a system capable of recognizing and grasping a particular isolated object may fail to recognize and grasp the same object in a cluttered environment.
The problem of refining the estimate of the pose given the identity of an object and its approximate pose is known as pose refinement. Pose refinement is a particular instance of a more general problem of registration of 3-D shapes. An example of its application is described by Burtnyk, N., and Basran, J., in Supervisory Control of Telerobots in Unstructured Environments, in Proceedings of 5th International Conference on Advanced Robotics, Piza, Italy, June 1991, pages 1420-1424, NRC 31833. In this application, an operator points to an object of interest on a video display of the task environment and provides its approximate pose by orienting a model of that object into a similar view. The operator instructs the robot to move over the object with an appropriate standoff and to use a wrist-mounted 3-D camera to refine the pose before grasping.
Relatively little work has been published in the area of registration, pose estimation, alignment, and motion estimation of 3-D free-form shapes and most of the existing publications address global shape matching or registration pertaining to limited classes of shapes.
Paul J. Besl and Neil D. McKay, in a paper entitled A Method For Registration of 3-D Shapes, published in IEEE Transaction on Pattern Analysis and Machine Intelligence Vol. 14(2) P. 239-256, 1992, have presented a comprehensive overview of the problems associated with pose registration. Besl and McKay address a wide variety of geometric shape representations, from point sets to free-form curves and surfaces and present the iterative closest point (ICP) method as a general solution. The solution is presented in the form of fitting a "sensed data" shape to a "given model" shape. The ICP method iteratively computes the distance of each data point to the closest point on the model and then determines the transformation that minimizes these distances. However, this solution is only of use for that class of problems where the entire data set corresponds to the model shape. It does not distinguish between data points that correspond to the model shape and those that do not. Therefore, if only a subset of the data set corresponds to the model shape, these "outliers" will affect the result and must be removed from the data in a preprocessing stage before the registration process is applied.
It is an object of this invention to provide a method for refining the pose of an object that may be situated in a cluttered background.